Group Q:

Laura Cuna, 20211312
Amelie Langenstein, 20210637
Tongjiuzhou Liu, 20211012
Nina Urbancic, 20211314

Business Case 4 - Cryptocurrency Value Prediction

Contents of this notebook

1. Business Understanding

1.1. Determine Business Objectives

1.2. Assess Situation

1.3. Determine Data Mining Goals

1.4. Produce Project Plan

2. Data Understanding

2.1. Collect Initial Data

2.2. Describe Data

2.3. Explore Data

Generate dataframe for each Cryptocurrency

2.4. Verify Data Quality

Data Visualization

Candlestick with Rangeslider

Daliy views of Ten Cryptocurrenies

Daliy High price of Ten Cryptocurrenies subplots

Daliy Volume of Ten Cryptocurrenies subplots

Boxplots of the daily diffrence between Value high and low

Boxplots of the daily diffrence between Value Open and close

Boxplots of the daily diffrence between Value adjust close and close

Plot the volume of Crytocurrency with Candlestick and Rangeslider

3. Data Preparation

3.1. Select Data

3.2. Clean Data

3.3. Construct Data and Integrate Data

close_off_high represents the gap between the closing price and price high for that day, where values of -1 and 1 mean the closing price was equal to the daily low or daily high, respectively. The volatility columns are simply the difference between high and low price divided by the opening price.

4. Modeling

4.1. Time Series Split Test

Naive Approach

Simple Average

Moving Average

Simple Exponential Smoothing

Holt’s Linear Trend method

Holt-Winters Method

ARIMA

4.2. Blocked Time Series Split Test

Naive Approach

Simple Average

Moving Average

Simple Exponential Smoothing

Holt’s Linear Trend method

Holt-Winters Method

ARIMA

4.3 short period blocked time series split

4.4 Build Time series model for the features that we are going to us in the final prediction model

Test the Timeseries model for BTC_close_off_high

Test the Timeseries model for BTC_volatility

Test the Timeseries model for BTC_day_diff

build suspective model

4.3. Build Model

4.4. Assess Model

5. Evaluation

6. Deployment